Effects of climatic factors on COVID-19 transmission in Ethiopia

Endeshaw, Fitsum Bekele; Getnet, Fentabil; Temesgen, Awoke Misganaw; Mirkuzie, Alemnesh H.; Olana, Latera Tesfaye; Alene, Kefyalew Addis; Birhanie, Solomon Kibret

doi:10.1038/s41598-022-24024-9

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Furthermore, it helps to know detailed information about the disease, for example, the pattern of the disease. As a result, health providers can plan and mobilize resources effectively [ 33 , 34 ]. Still, there is no study on this topic in Benishangul Gumuz Reginal State (BGRS) of Ethiopia particularly in the Assosa COVID-19 treatment center.…”

Section: Introductionmentioning

confidence: 99%

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

Kassie,

Gobena,

Alemu

et al. 2023

Pneumonia

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Background The Novel Coronavirus disease (COVID-19) pandemic has become a global threat. Determining the time to recovery from COVID-19 is intended to assist healthcare professionals in providing better care, and planning logistics. So, the study aimed to identify the factors that affect the time to recovery from COVID-19 for patients treated at Assosa COVID-19 treatment center, Benishangul Gumuz Regional State, Western Ethiopia. Methods A retrospective study design was conducted on 334 randomly selected COVID-19 patients at Assosa COVID-19 treatment center from February 2021 to July 2021. The median survival time, Kaplan–Meier survival estimate, and Log-Rank test were used to describe the data and compare the survival time between groups. The study used the Cox PH model to analyze the time to the first recovery of COVID-19 patients, where hazard ratio, p-value, and 95% CI for hazard ratio were used for testing significance. Schoenfeld and Cox-Snell residuals were used for checking the model assumption. Results The overall incidence rate was 13.79 per 100 (95% CI: 10.04, 18.95) person-days observations. The median time to recovery was 16 days. At the end of the follow-up, 77.2% of the patients had developed an event of recovery, and the rest 22.8% were censored. The mean age of patients was 45.22 years. Severe COVID-19 patients (AHR = 0.7876, 95% CI: 0.7090, 0.8748), presence of symptoms (AHR = 0.2814, 95% CI: 0.1340, 0.5914), comorbidity (AHR = 0.1627, 95% CI: 0.1396, 0.1897), ≥ 90 oxygen saturation (AHR = 3.2370, 95% CI: 2.161, 4.848), and being older age (AHR = 0.9840, 95% CI: 0.971, 0.9973) were found to have statistically significant association with the time to recovery from COVID-19. Conclusion The study concludes that severe COVID-19 patients, male patients, patients having comorbidity, older age, and patients having symptoms as poor prognostic factors of COVID-19 disease and also prolonged recovery time. Therefore, health providers in treatment centers should give strict follow-up and priority to older patients, severe COVID-19 patients, and patients having another co-morbid illness by focusing on respiratory difficulties and underlying pre-existing medical conditions to manage the disease severity and recover quickly.

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Section: Introductionmentioning

confidence: 99%

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

Kassie,

Gobena,

Alemu

et al. 2023

Pneumonia

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The Effect of Seasonal Factors on the Spread and Mortality of COVID-19: Retrospective Multicenter Study

ARIKAN

Yamanoğlu

Tekindal

et al. 2023

HUSBFD

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Objectives: The impact of seasonal factors on the spread of Coronavirus-19 disease (COVID-19) is not yet clear. The aim of this study is to determine the effect of seasonal factors on the spread of COVID-19. Methods: This multicenter retrospective study was performed by collecting 284-day COVID-19 data from two university hospitals in a metropolitan center. Correlations between the seasonal parameters of temperature, humidity, wind, and rainfall and the spread of COVID-19 and its clinical outcomes were evaluated using Spearman’s correlation test. Since no linear relationship was determined between variables exhibiting correlation, all models were tested using non-linear curve estimation regression models. The most powerful of the curve estimation regression models, capable of explaining more than 20% of the changes in COVID-19 parameters, was formulated to explain the expected number of events. Results: A total of 24 225 patients were included in the study. The most powerful correlation was between mean daily temperature and daily case numbers (r:-0.643, p<0.00), with case numbers being highest on days when the mean temperature was 7-18℃. Mean temperate was capable of explaining 57% of COVID-19 case numbers (R-Square:0.571, p<0.00), the relationship between them being best explained in the ’S’ curve regression model. The formula ‘’Y=exp(2.07+31.34/x)’’ was obtained for the number of patients expected from the model according to mean temperature. Conclusions: Temperature may be the most effective factor in the spread of COVID-19 and the number of cases may be predicted based on temperature.

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Impact of Air Pollutants and Meteorological Parameters on Severe Pneumonia in Older Adults

Safçi,

Aybal,

Gungor

2024

Preprint

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Background This study aimed to investigate the relationship between air pollutants, weather parameters, and the characteristics and mortality of pneumonia in the older adult population. Methods This retrospective, observational, cross-sectional study included a total of 388 patients. Air pollutants and meteorological parameters were retrieved from official databases. Statistical tests were conducted using correlation and binary logistic regression analyses. Results The mean age of the 388 patients aged was 77.63 years. PSI (pneumonia severity index) was inversely correlated with nitric oxide (NO) exposure and positively correlated with ozone (O3) exposure (r = -0.199 and r = 0.313, respectively). An increase in humidity and wind speed over three days was positively correlated with PSI (r = 0.319 and r = 0.144, respectively). Increased O3 exposure during the three- and seven-day periods preceding the patient visit increased the risk of death at a similar rate (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.01–1.03). Increased humidity exposure was also associated with higher mortality (OR: 1.06, 95% CI: 1.01–1.11). Conclusion Meteorological changes and air pollution are factors that can increase the severity and mortality risk of pneumonia in the older adult population. Increased wind speed can elevate pneumonia severity, while high O3 and humidity exposure can heighten both the severity and mortality associated with the disease. Ethical Approval Number and Date: This study received approval from the Ethics Committee of Adana City Research and Education Hospital with protocol number 3263 (date: March 28, 2024).

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Effects of climatic factors on COVID-19 transmission in Ethiopia

Cited by 3 publications

References 35 publications

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

Time to recovery and its determinant factors among patients with COVID-19 in Assosa COVID-19 treatment center, Western Ethiopia

The Effect of Seasonal Factors on the Spread and Mortality of COVID-19: Retrospective Multicenter Study

Impact of Air Pollutants and Meteorological Parameters on Severe Pneumonia in Older Adults

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